Integrating neural networks and knowledge-based systems for robotic control

Handelman, David A.; Lane, Stephen H.; Gelfand, Jack

doi:10.1109/robot.1989.100184

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…This approach is especially relevant in fields like aerospace engineering, where systems often exhibit complex nonlinear behaviors. The deep learning methods are also implemented for fault diagnosis and predictive maintenance (to detect subtle patterns or anomalies in system data that may indicate a fault or a forthcoming failure), distributed control systems (to optimize communication and control strategies among distributed agents), human-in-the-loop control systems (to model human decision-making processes and incorporating these models into control systems for more intuitive and efficient human-machine interactions) [Handelman et al, 1989;van Luenen, 1995;Esfandiari et al, 2021].…”

Section: A-lyapunov's Indirect Methodmentioning

confidence: 99%

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Uyulan

2023

Preprint

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This paper presents a deep neural network (DNN) based method to estimate approximate Lyapunov functions and their orbital derivatives, which are key to the stability of the system in control theory. Our approach addresses the challenge of the curse of dimensionality in control and optimization problems, demonstrating that the computational effort required grows only polynomically with the state dimension. This is a significant improvement over traditional methods. We emphasize that the calculated functions are approximations of Lyapunov functions and not exact representations. This distinction is important, as validating these approximations in high-dimensional environments is challenging and opens new avenues for future research. Our approach diverges from traditional grid-based approaches and moves away from relying on small-gain theorems and accurate subsystem knowledge. This flexibility is proven in deriving Lyapunov functions for the development of stabilizing feedback rules in nonlinear systems. A crucial aspect of our approach is the use of ReLU activation features in neural networks, which is steady with modern deep getting-to-know traits. We also explore the feasibility of using DNNs to estimate fairly constructive Lyapunov functions, despite the demanding situations posed through uncertainty. Our set of rules' outcomes aren't unique, highlighting the want to set up criteria for figuring out especially useful Lyapunov features. The paper culminates with the aid of emphasizing the capacity of DNNs to approximate compositional Lyapunov capabilities, especially underneath small-gain conditions, to mitigate the curse of dimensionality. Our contributions are manifold, including scalability in dealing with systems of various dimensionality, flexibility in accommodating both low and excessive-dimensional structures, and performance in computing Lyapunov features through deep learning strategies. However, challenges continue to be in the approximation accuracy and the verification of Lyapunov functions in better dimensions.

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Section: A-lyapunov's Indirect Methodmentioning

confidence: 99%

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Uyulan

2023

Preprint

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“…Tsutsumi and Matsumoto (1987) have also developed a method for finding the optimal path for a 2D snake manipulator through sets of obstacles using energy minimization with a Hopfield network. A rule and CMAC controller system was designed by Handelman et al (1989) to control a tennis-like swing of a manipulator.…”

Section: Novel Applicationsmentioning

confidence: 99%

“…Some researchers have used the Adaline (Tolat and Widrow, 1988). Another common network paradigm is the CMAC (Miller, 1988;Handelman et al, 1989;Miller et al, 1990).…”

Section: Summary Of Neural-network-based Controlmentioning

confidence: 99%

“…Some researchers developed learning rules of their own (Nagata et al, 1988), and others used non-homogeneous network structures (Elsley, 1988;Nagata et al, 1988) which give better results, but are less desirable because they do not hold true to biological models. Some other researchers have taken a novel approach to solving network problems by including external rule-based controls (Tsutsumi and Matsumoto, 1987;Suddarth et al, 1988;Handelman et al, 1989). The rule-based control allows the advantages of neural and von Neumann computing schemes to be obtained.…”

Section: Summary Of Neural-network-based Controlmentioning

confidence: 99%

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Neural networks and the inverse kinematics problem

et al. 1993

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Inverse kinematics is a fundamental problem in robotics. Past solutions for this problem have been realized through the use of various algebraic or algorithmic procedures. In this paper the use of feedforward neural networks to solve the inverse kinematics problem is examined for three different cases. A closed kinematic linkage is used for mapping input joint angles to output joint angles. A three-degree-of-freedom manipulator in 3D space is used to test mappings from both cartesian and spherical coordinates to manipulator joint coordinates. A majority of the results have average errors which fall below 1% of the robot workspace. The accuracy indicates that neural networks are an alternate method for performing the inverse kinematics estimation, thus introducing the fault-tolerant and high-speed advantages of neural networks to the inverse kinematics problem.This paper also shows the use of a new technique which reduces neural network mapping errors with the use of error compensation networks. The results of the work are put in perspective with a survey of current applications of neural networks in robotics.

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Neural Network Controllers

Pham

Liu

1995

Neural Networks for Identification, Prediction and Control

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Integrating neural networks and knowledge-based systems for robotic control

Cited by 8 publications

References 13 publications

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Efficient Computation of Lyapunov Functions Using Deep Neural Networks for the Assessment of Stability in Controller Design

Neural networks and the inverse kinematics problem

Neural Network Controllers

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